Pacs viewer and a method for identifying patient orientation

ABSTRACT

A PACS viewer and an associated method and computer program product are provided for identifying patient orientation. With respect to a PACS viewer, the PACS viewer includes processing circuitry configured to register an image onto a template. The processing circuitry is also configured to determine a representative seed point within each of the left and right lungs as represented by the image. The processing circuitry is further configured to apply an active contour model to each of the left and right lungs to generate a binary image of the left and right lungs. In this regard, the processing circuitry is configured to apply the active contour model by initializing the active contour model with a mask constructed using the representative seed points. Further, the processing circuitry is configured to detect at least one feature from the binary image to permit identification of the left and right lungs.

TECHNOLOGICAL FIELD

An example embodiment relates generally to a picture archiving andcommunications system (PACS) viewer and associated method and, moreparticularly, to a PACS viewer and associated method for identifyingpatient orientation.

BACKGROUND

Images of a patient are captured with a wide range of modalitiesincluding, for example, computerized tomography (CT), magnetic resonanceimaging (MRI), computed radiography (CR), digital radiography (DR) andmammography (MG). In order to properly assess the image and tosubsequently treat the patient based upon information gathered from theimage, the orientation of the patient at the time that the image wascaptured is of import. By determining the orientation of the patient,specific features of the patient are able to be identified as beingeither on the left side or on the right side of the patient. Forexample, the left lung may be distinguished from the right lung suchthat any subsequent treatment of the patient to address a lung conditionthat exists in only one of the lungs is focused upon the correct lung.

The orientation of the patient may be identified in various manners. Inone example, a technician may place a designation, such as an L or an R,on or proximate to the patient so as to designate the left side or theright side of the patient, respectively. The designation will becaptured within the image and will provide an indication to technicians,radiologists or other healthcare practitioners who subsequently reviewthe image study as to the orientation of the patient. Alternatively,following the capture of an image, a technician may insert a designationinto the image itself, such as by burning an L or an R designating leftside or right side of the patient, respectively, into the image. Byinserting the designation into the image, a technician, a radiologist orother healthcare practitioner who reviews the image study will be ableto determine the orientation of the patient at the time that the imagewas captured. Still further, an indication may be inserted into theheader of a medical image study that indicates the orientation of thepatient at the time that the image was captured. The indication that isinserted into the header may be based upon a predefined protocol inwhich the patient is assumed to be in a particular orientation at thetime that the image was captured. For example in posteroanterior (PA)chest exams in computed radiography, the imaging plate is placed infront of the patient with the x-ray beam originates from behind thepatient. This arrangement results in an image in which the left side ofthe patient is displayed on the right side of the image as if thepatient is standing in front of the physician.

While the foregoing techniques are useful in identifying the orientationof a patient at the time that an image was captured, in instances inwhich a designation is to be associated with the image, a technician maysometimes forget to place a designation proximate the patient at thetime that an image is captured or may forget to burn a designation intothe image. As such, it may be difficult to determine the orientation ofthe patient during subsequent review of the medical image study, atleast with the degree of accuracy that is desired. Alternatively, ininstances in which an indication of the orientation is to be insertedinto the head of an image study, the patient may not be oriented inaccordance with the predefined protocol at the time that image iscaptured such that the resulting designation of the orientation that isincluded within the header of the medical image study willcorrespondingly be inaccurate with such inaccuracies potentially leadingto undesired issues associated with the subsequent treatment of thepatient based upon a review of the medical image study.

BRIEF SUMMARY

A PACS viewer and an associated method and computer program product areprovided in accordance with an example embodiment for identifyingpatient orientation in a reliable and automated manner. For example, thePACS viewer and associated method and computer program product of anexample embodiment are configured to discriminate between the left andright lungs of a patient based upon an image of the patient withoutreliance upon placement of a designation proximate the patient at thetime that the image was captured, burning a designation into an image ororientation of the patient at the time of image capture according to apredefined protocol. As such, the PACS viewer and associated method andcomputer program product offer reliable identification of the left andright lungs of a patient while providing greater freedom in terms of theorientation of the patient at the time that the image is captured. Byreliably identifying the left and right lungs of the patient from theimage, the PACS viewer and associated method and computer programproduct may avoid issues associated with subsequent treatment of apatient based upon a medical image study in which the orientation of thepatient was lacking or incorrect.

In an example embodiment, a PACS viewer is provided that includesprocessing circuitry configured to register an image onto a template.The processing circuitry is also configured to determine arepresentative seed point within each of the left and right lungs asrepresented by the image. The processing circuitry is further configuredto apply an active contour model to each of the left and right lungs togenerate a binary image of the left and right lungs. In this regard, theprocessing circuitry is configured to apply the active contour model byinitializing the active contour model with a mask constructed using therepresentative seed points. Further, the processing circuitry of thisexample embodiment is configured to detect at least one feature from thebinary image to permit identification of the left and right lungs.

The processing circuitry of an example embodiment is configured todetect at least one feature of the binary image by determining an areaof the left and right lungs from the binary image and by identifying theright lung to have the larger area. The processing circuitry of anotherexample embodiment is configured to detect at least one feature from thebinary image by defining a line between left and right lungs asrepresented by the binary image. The line is located based upon adiaphragm of one of the lungs. The processing circuitry of this exampleembodiment is also configured to divide the line into first and secondline segments extending between a midline between the left and rightlungs and the respective lung. The processing circuitry of this exampleembodiment is further configured to compare the length of each of thefirst and second line segments and to identify the left lung to be onthe same side of the midline as the longer of the first and second linesegments. The processing circuitry of yet another example embodiment isconfigured to detect at least one feature from the binary image bymaking a first comparison of the binary image to the template and makinga second comparison of a mirrored version of one of the binary image orthe template to the other one of the binary image or the template. Thetemplate represents a predefined orientation with respect to the leftand right lungs. The processing circuitry of this example embodimentalso determines which of the first comparison or the second comparisonis indicative of more similarity and identifies the left and right lungswithin the binary image based upon the predefined orientation of thetemplate and the one of the first or second comparison that isindicative of more similarity.

The processing circuitry of an example embodiment is configured todetermine a representative seed point by defining a plurality of linesextending across the image and to determine, within each of the left andright lungs, a seed point on each of the plurality of lines. Theprocessing circuitry of this example embodiment is also configured todetermine the representative seed point within each of the left andright lungs from among the seed points determined on each of theplurality of lines within each of the left and right lungs. Theprocessing circuitry of an example embodiment is further configured tosubject the image to anisotropic diffusion prior to applying the activecontour model. The processing circuitry of an example embodiment is alsoconfigured to predict patient orientation based upon the identificationof the left and right lungs utilizing a support vector machineclassifier. In this example embodiment, the processing circuitry isfurther configured to compare the patient orientation that is predictedto an indication of orientation that is associated with the image and tocause a notification to be provided in an instance in which the patientorientation that is predicted is different than the indication oforientation that is associated with the image.

In another example embodiment, a method for identifying patientorientation is provided that includes registering an image onto atemplate. The method also includes determining a representative seedpoint within each of the left and right lungs as represented by theimage. The method further includes applying an active contour model toeach of the left and right lungs to generate a binary image of the leftand right lungs. In this regard, the method applies the active contourmodel by initializing the active contour model with a mask constructedusing the representative seed points. The method of this exampleembodiment also includes detecting at least one feature from the binaryimage to permit identification of the left and right lungs.

The method of an example embodiment detects at least one feature fromthe binary image by determining an area of the left and right lungs fromthe binary image and by identifying the right lung to have the largerarea. The method of another example embodiment detects at least onefeature from the binary image by defining a line between the left andright lungs as represented by the binary image. The line is locatedbased upon a diaphragm of one of the lungs. The method of this exampleembodiment also includes dividing the line into first and second linesegments extending between a midline between the left and right lungsand a respective lung. The method of this embodiment further includescomparing a length of each of the first and second line segments andidentifying the left lung to be on the same side of the midline as thelonger of the first and second line segments. The method of anotherexample embodiment detects at least one feature from the binary image bymaking a first comparison of the binary image to the template and makinga second comparison of a mirrored version of one of the binary image orthe template to the other one of the binary image or the template. Thetemplate represents a predefined orientation with respect to the leftand right lungs. The method of this example embodiment also determineswhich of the first comparison or the second comparison is indicative ofmore similarity and identifies the left and right lungs within thebinary image based upon the predefined orientation of the template andthe one of the first or second comparison that is indicative of moresimilarity.

The method of an example embodiment determines a representative seedpoint by defining a plurality of lines extending across the image anddetermining, within each of the left and right lungs, a seed point oneach of the plurality of lines. The method of this example embodimentalso includes determining the representative seed point within each ofthe left and right lungs from among the seed points determined on eachof the plurality of lines within each of the left and right lungs. Themethod of an example embodiment also includes subjecting the image toanisotropic diffusion prior to applying the active contour model. Themethod of an example embodiment also includes predicting patientorientation based upon the identification of the left and right lungsutilizing a support vector machine classifier. The method of thisexample embodiment also includes comparing the patient orientation thatis predicted to an indication of orientation that is associated with theimage and causing a notification to be providing in an instance in whichthe patient orientation that is predicted is different than theindication of orientation that is associated with the image.

In a further example embodiment, a computer program product is providedfor identifying patient orientation. The computer program productincludes a non-transitory computer readable storage medium havingprogram code portions stored therein with the program code portionsconfigured, upon execution, to register an image onto a template. Thecomputer program product also includes program code portions configuredto determine a representative seed point within each of the left andright lungs as represented by the image and program code portionsconfigured to apply an active contour model to each of the left andright lungs to generate a binary image of the left and right lungs. Inthis regard, the program code portions configured to apply the activecontour model include program code portions configured to initialize theactive contour model with a mask constructed using the representativeseed points. The computer program product of this example embodimentalso includes program code portions configured to detect at least onefeature from binary image to permit identification of the left and rightlungs.

The program code portions configured to detect at least one feature fromthe binary image include, in an example embodiment, program codeportions configured to determine an area of the left and right lungsfrom the binary image and program code portions configured to identifythe right lung to have the larger area. In another example embodiment,the program code portions configured to detect at least one feature fromthe binary image includes program code portions configured to define aline between the left and right lungs as represented by the binaryimage. The line is located based upon a diaphragm of one or the lungs.The program code portions of this example embodiment are also configuredto divide the line into first and second line segments extending betweena midline between the left and right lungs and the respective lung. Theprogram code portions of this example embodiment are further configuredto compare a length of each of the first and second ling segments and toidentify the left lung to be on the same side of the midline as thelonger of the first and second line segments. In a further exampleembodiment, the program code portions configured to detect at least onefeature from the binary image include program code portions configuredto make a first comparison of the binary image to the template and tomake a second comparison of a mirrored version of one of the binaryimage or the template to the other one of the binary image or thetemplate. The template represents a predefined orientation with respectto the left and right lungs. In this example embodiment, the programcode portions configured to detect at least one feature from the binaryimage also include program code portions configured to determine whichof the first comparison or the second comparison is indicative of moresimilarity and program code portions configured to identify the left andright lungs within the binary image based upon the predefinedorientation of the template and the one of the first or secondcomparison that is indicative of more similarity.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described aspects of the present disclosure in generalterms, reference will now be made to the accompanying drawings, whichare not necessarily drawn to scale, and wherein:

FIG. 1 is a block diagram of a system including a PACS viewer configuredto receive images captured by a plurality of imaging modalities;

FIG. 2 is a block diagram of a PACS viewer that may be specificallyconfigured in accordance with an example embodiment of the presentinvention;

FIG. 3 is a flowchart illustrating operations performed, such as by thePACS viewer of FIG. 2, in accordance with an example embodiment of thepresent invention;

FIG. 4 is an image of a template in accordance with an exampleembodiment of the present invention;

FIG. 5 is an image in which a plurality of lines have been defined thatextend thereacross in accordance with an example embodiment of thepresent invention;

FIG. 6 is a graphical representation of the pixel values along arespective line depicted in FIG. 5;

FIG. 7 is an image in which seed points are defined along each of thelines and a representative seed point is determined from among theplurality of seed points in accordance with an example embodiment of thepresent invention;

FIG. 8 is a mask constructed using the representative seed points inaccordance with an example embodiment of the present invention;

FIG. 9 is a binary image generated by application of an active contourmodel in accordance with an example embodiment of the present invention;

FIG. 10 is a binary image following post-segmentation processing inaccordance with an example embodiment of the present invention;

FIG. 11 depicts a technique for identifying the diaphragm associatedwith the left and right lungs in accordance with an example embodimentof the present invention;

FIG. 12 is a binary image in which the left and right lungs areidentified based upon a line defined by the diaphragm of one of thelungs in accordance with an example embodiment of the present invention;

FIG. 13 is a graphical representation of a comparison of the binaryimage and the template in accordance with an example embodiment of thepresent invention; and

FIG. 14 is a graphical representation of a comparison of the binaryimage and a mirrored version of the template in accordance with anexample embodiment of the present invention.

DETAILED DESCRIPTION

Some embodiments of the present invention will now be described morefully hereinafter with reference to the accompanying drawings, in whichsome, but not all embodiments of the invention are shown. Indeed,various embodiments of the invention may be embodied in many differentforms and should not be construed as limited to the embodiments setforth herein; rather, these embodiments are provided so that thisdisclosure will satisfy applicable legal requirements. Like referencenumerals refer to like elements throughout. As used herein, the terms“data,” “content,” “information” and similar terms may be usedinterchangeably to refer to data capable of being transmitted, receivedand/or stored in accordance with embodiments of the present invention.Thus, use of any such terms should not be taken to limit the spirit andscope of embodiments of the present invention.

A method, computing device and computer program product are provided inaccordance with an example embodiment of the present invention in orderto determine patient orientation at the time of that an image of aportion of the patient is captured. The image may be a medical image,such as an image of a patient. The image may have been captured by anyof a wide variety of different modalities, such as CT, MRI, CR, DR, MGor the like. The image may be utilized for a wide variety of differentpurposes including diagnosis, treatment, training or the like. Stillfurther, in an instance in which the image is an image of a portion of apatient, the image may be of any of a wide variety of different portionsof the patient, such as a limb, organ or the like. For purposes ofexplanation, however, an image of the patient's chest showing the leftand right lungs of the patient will be hereinafter discussed.

By way of example, FIG. 1 illustrates a system 20 that may benefit froman example embodiment of the present invention. As shown, the systemincludes one or more imaging modalities (three example modalities beingshown as modalities 22 a, 22 b and 22 c) for acquiring an image, such asan image of the human body or parts of the human body for clinicalpurposes such as examination, diagnosis and/or treatment. Examples ofsuitable modalities include, for example, CT, MRI, CR, DR, MG or thelike. The system also includes a PACS server 26 for receiving andprocessing the image studies from one or more of the imaging modalitiesand for storing the image studies, such as in image storage 28. Thesystem of FIG. 1 further includes a viewing station, such as a PACSviewer 24, configured to receive an image study from the PACS server,and present the images of the image study such as for review by amedical professional such as a radiologist.

The imaging modalities 22, the PACS viewer 24, the PACS server 26 and/orthe image storage 28 may be configured to directly and/or indirectlycommunicate with one another in any of a number of different mannersincluding, for example, any of a number of wireline or wirelesscommunication or networking techniques. Examples of such techniquesinclude, without limitation, Universal Serial Bus (USB), radio frequency(RF), Bluetooth (BT), infrared (IrDA), any of a number of differentcellular (wireless) communication techniques such as any of a number of2G, 2.5G, 3G, 4G or Long Term Evolution (LTE) communication techniques,local area network (LAN), wireless LAN (WLAN) techniques or the like. Inaccordance with various ones of these techniques, the imaging modality,the PACS viewer, the PACS server and/or the image storage can be coupledto and configured to communicate across one or more networks. Thenetwork(s) can comprise any of a number of different combinations of oneor more different types of networks, including data and/or voicenetworks. For example, the network(s) can include one or more datanetworks, such as a LAN, a metropolitan area network (MAN), and/or awide area network (WAN) (e.g., Internet), and include one or more voicenetworks, such as a public-switched telephone network (PSTN). Althoughnot shown, the network(s) may include one or more apparatuses such asone or more routers, switches or the like for relaying data, informationor the like between the imaging modality, viewing station and/orcomputing apparatus.

The PACS viewer 24 of an example embodiment is embodied by computingdevice, such as a computer workstation, a personal computer, a tabletcomputer, a laptop computer, a mobile terminal, e.g., a smartphone, orother type of computing device that includes or is associated with botha display and the processing circuitry for performing the variousfunctions described hereinafter. The computing device of a PACS viewer24 in accordance with an example embodiment is depicted in FIG. 2. Asshown, the PACS viewer includes or is associated and in communicationwith processing circuitry 32 that is configurable to perform functionsin accordance with one or more example embodiments disclosed herein. Inthis regard, the processing circuitry may be configured to performand/or control performance of one or more functionalities of thecomputing device in accordance with various example embodiments, andthus may provide means for performing functionalities of the computingdevice. The processing circuitry may be configured to perform dataprocessing, application execution and/or other processing and managementservices according to one or more example embodiments.

In some example embodiments, the processing circuitry 32 includes aprocessor 34 and, in some embodiments, such as that illustrated in FIG.3, further includes memory 36. The processing circuitry may be incommunication with or otherwise control a user interface 38, such as adisplay, a touchscreen, a touch surface, a keyboard and/or otherinput/output mechanisms and, in some embodiments, may also optionallyinclude a communication interface 39 for communicating with othercomputing systems. As such, the processing circuitry may be embodied asa circuit chip (e.g., an integrated circuit chip) configured (e.g., withhardware, software or a combination of hardware and software) to performoperations described herein.

The processor 34 may be embodied in a number of different ways. Forexample, the processor may be embodied as various processing means suchas one or more of a microprocessor or other processing element, acoprocessor, a controller or various other computing or processingdevices including integrated circuits such as, for example, an ASIC(application specific integrated circuit), an FPGA (field programmablegate array), or the like. Although illustrated as a single processor, itwill be appreciated that the processor may comprise a plurality ofprocessors. The plurality of processors may be in operativecommunication with each other and may be collectively configured toperform one or more functionalities of the computing device as describedherein. The plurality of processors may be embodied on a singlecomputing device or distributed across a plurality of computing devicescollectively configured to function as the computing device. In someexample embodiments, the processor may be configured to executeinstructions stored in the memory 36 or otherwise accessible to theprocessor. As such, whether configured by hardware or by a combinationof hardware and software, the processor may represent an entity (e.g.,physically embodied in circuitry—in the form of processing circuitry 32)capable of performing operations according to embodiments of the presentinvention while configured accordingly. Thus, for example, when theprocessor is embodied as an ASIC, FPGA or the like, the processor may bespecifically configured hardware for conducting the operations describedherein. Alternatively, as another example, when the processor isembodied as an executor of software instructions, the instructions mayspecifically configure the processor to perform one or more operationsdescribed herein.

The processing circuitry 32 may also include memory 36 as shown in FIG.3. In some example embodiments, the memory may include one or morenon-transitory memory devices such as, for example, volatile and/ornon-volatile memory that may be either fixed or removable. In thisregard, the memory may comprise a non-transitory computer-readablestorage medium. It will be appreciated that while the memory isillustrated as a single memory, the memory may comprise a plurality ofmemories. The plurality of memories may be embodied on a singlecomputing device or may be distributed across a plurality of computingdevices collectively configured to function as the computing device. Thememory may be configured to store information, data, applications,instructions and/or the like for enabling the computing device to carryout various functions in accordance with one or more exampleembodiments. For example, the memory may be configured to buffer inputdata for processing by the processor 34. Additionally or alternatively,the memory may be configured to store instructions for execution by theprocessor. As yet another alternative, the memory may include one ormore databases that may store a variety of files, contents or data sets,such as medical images, e.g., image studies, for a plurality ofpatients. Among the contents of the memory, applications may be storedfor execution by the processor in order to carry out the functionalityassociated with each respective application. In some cases, the memorymay be in communication with one or more of the processor or the userinterface 38 via a bus or buses for passing information among componentsof the computing device.

In addition to the processing circuitry 32, the PACS viewer 24 mayinclude a user interface 38 for displaying and/or receiving data,content or the like. The user interface may include one or moreearphones and/or speakers, a display, and/or a user input interface. Theuser interface, in turn, can include any of a number of devices allowingthe computing device to receive data from a user, such as a microphone,a keypad, a touch-sensitive surface (integral or separate from themonitor), a joystick, or other input device. As will be appreciated, theprocessing circuitry may be directly connected to other components ofthe computing device, or may be connected via suitable hardware. In oneexample, the processing circuitry may be connected to the user interfacevia an adapter configured to permit the processing circuitry to sendgraphical information to the user interface.

The PACS viewer 24 of an example embodiment may also include acommunication interface 39 that may be any means such as a device orcircuitry embodied in either hardware or a combination of hardware andsoftware that is configured to receive and/or transmit data from/toother electronic devices in communication with the apparatus, such as bybeing configured to receive medical image studies from the PACS server26. In this regard, the communication interface may include, forexample, an antenna (or multiple antennas) and supporting hardwareand/or software for enabling communications with a wirelesscommunication network. Additionally or alternatively, the communicationinterface may include the circuitry for interacting with the antenna(s)to cause transmission of signals via the antenna(s) or to handle receiptof signals received via the antenna(s). In some environments, thecommunication interface may alternatively or also support wiredcommunication.

Having now described a PACS viewer 24 configured to implement and/orsupport implementation of various example embodiments, features ofseveral example embodiments will now be described. It will beappreciated that the following features are non-limiting examples offeatures provided by some example embodiments. Further, it will beappreciated that embodiments are contemplated within the scope ofdisclosure that implement various subsets or combinations of thefeatures further described herein. Accordingly, it will be appreciatedthat some example embodiments may omit one or more of the followingfeatures and/or implement variations of one or more of the followingfeatures.

Referring now to FIG. 3, the operations performed, such as by the PACSviewer 24 of FIG. 2, in order to determine patient orientation at thetime that an image of a portion of the patient is captured areillustrated in accordance with an example embodiment of the presentinvention. As shown in block 40, the PACS viewer of an exampleembodiment includes means, such as the processing circuitry 32, theprocessor 34 or the like, for registering an image onto a template. Theimage may be registered onto the template by an iterative process inwhich affine transformation parameters (translation, rotation andscaling parameters) are updated based on step gradient descent andMattes mutual information optimization techniques. By registering theimage onto a template, background and other unwanted regions of theimage may be removed and the image may be straightened so as tofacilitate subsequent processing. By way of example, x-ray images of apatient's chest may include regions, such as shoulders or externalbackgrounds, that are unwanted during an analysis of the patient'slungs. As such, registration of the image unto the template may removethese unwanted regions. Additionally, in some x-ray images, the spinemay be slanted to one side rather than being upright with the slantingof the spine potentially causing issues in the subsequent processing ofthe image. Thus, the registration of the image to the template maystraighten the image.

The image to be registered in an image of a patient that has beencaptured by an image modality, such as a two dimensional projectionimage captured by a computed radiography modality or a digitalradiography modality. The image is typically provided by a PACS server26 and may be one of a plurality of images provided as an image study.In addition to the images themselves, an image study generally includesadditional information, such as may be provided by a header, regardingthe image study and the respective images. Among other information, theheader of an image study may include information identifying theorientation of the patient at the time that the images of the imagestudy were captured. In this regard, the indication provided by theheader may be based upon a predefined protocol in which the patient ispresumed to have been in a predefined orientation at the time that theimages were captured.

The template is an intensity template image of the same portion of thepatient's body that is represented by the images received from the PACSserver 26. By way of example, the image to be registered to the templatemay an image of the patient's chest so as to show the patient's left andright lungs. This template may be constructed in various manners, but itis generally an unbiased anatomical template. In one example, aplurality of images of various patients' chests are processed andcombined to generate the template. For example, a plurality of chestx-rays may be cropped around the lungs, the cropped images may be addedand an average image may then be obtained. The plurality of images maythen be registered onto the average image, one at a time. The average ofthe registered images may then define the template.

The PACS viewer 24 of an example embodiment also includes means, such asthe processing circuitry 32, the processor 34 or the like, fordetermining a representative seed point within each of the left andright lungs as represented by the image, typically followingregistration onto a template. See block 42 of FIG. 3. In order todetermine the seed points inside the left and right lungs as representedby the image, the image may initially be inverted, in terms of the grayscale values of the pixels that comprise the image, such that the lungsappear darker than the remainder of the patient's chest. The processingcircuitry of this example embodiment is then configured to define aplurality of lines 60 extending across the image, such as plurality ofhorizontal lines as shown in FIG. 5. Although shown to extendhorizontally, the lines may extend in other directions so long as thelines pass through both the lungs as represented by the image.

The intensity of the pixels of the image along a line 60 variesdepending upon the gray scale level associated with the respectivepixels. For Monochrome 2 images in which the smallest pixel value isdisplayed as fully black and the largest pixel value is displayed asfully black, the greater intensity values are associated with thoseregions outside of the lungs as those regions are shown with a lightercolor and smaller intensity values within the lungs as the lungs areshown in a darker color. In this regard, FIG. 6 graphically depicts theintensity values along a line that extends across the left and rightlungs as representative by the image. Other types of images maydifferently represent the pixel values such that FIG. 6 is shown by wayof example, but not of limitation. For example, a Monochrome 1 imagecauses a pixel having the smallest value to be displayed as fully whiteand a pixel having the largest value to be displayed as fully black.This results in the intensity of the pixels of the image along line 60in a Monochrome 1 image being the inverse of what is shown in FIG. 6.Local maxima in a Monochrome 2 image will be local minima in aMonochrome 1 image and vice versa. As a point of comparison, in aninstance in which the Monochrome 1 and Monochrome 2 images of a lung arerepresented by ten bits which results in a maximum value of 4095, thepixel values of the lung in one example of a Monochrome 2 image may bein the range of 20-50, while the pixel values of the lung in one exampleof a Monochrome 1 image may be in a corresponding range of(4095−50=4045) to (4095−20=4075). The following discussion will be basedupon a Monochrome 2 image, but other types of images may alternativelybe utilized as described above with the reliance upon the Monochrome 2image being by way of example, but not of limitation. In order to obtaina smoother curve of the intensity values along a line across the lungs,the processing circuitry 32 of an example embodiment is configured todetermine the average intensity of the pixels along a respective lineand to then fit a Fourier model, such as an eight-term Fourier model, tothe pixel values along the respective line.

As shown, the processing circuitry 32 of an example embodiment isconfigured to identify both local maxima 62, 66, 70 and 74 and localminima 64, 68 and 72 from among the pixels values along a respectiveline 60 across the lungs. The processing circuitry of this exampleembodiment then identifies the two widest troughs 76, 78 of the graphthat include a local minima to correspond to the left and right lungs.Further, the processing circuitry of this example embodiment identifiesthe local minima within each trough along a respective line to be a seedpoint. Thus, along a respective line, one seed point is generallydefined within the left lung and another seed point is generally definedwithin the right lung with each of the seed points representing a localminima within one of the two widest troughs along the respective line.The processing circuitry of an example embodiment is configured torepeat this procedure along each of the lines that extend across theimage so as to determine seed points 80 on each of the plurality oflines within each of the left and right lungs, as shown in FIG. 7.

The processing circuitry 32 of this example embodiment is alsoconfigured to determine a representative seed point 82 within each ofthe left and right lungs from among these seed points 80 determined oneach of the plurality of lines 60. By way of example, the processingcircuitry of an example embodiment is configured to determine the medianof the seed points within the left lung and the median of the seedpoints within the right lung. For example, FIG. 7 depicts each of theseed points identified along the lines that extends across the image aswell as the representative seed points within the left and right lungsas determined by the medians of the seed points with a respectable lung.As also shown in FIG. 7, the processing circuitry of an exampleembodiment is configured to define a region, such as a square-shapedregion, around each representative seed point. The region may have apredefined size.

In block 44 of FIG. 3, the PACS viewer 24 of an example embodiment alsoinclude means, such as the processing circuitry 32, the processor 34 orthe like, for subjecting the image to anisotropic diffusion prior toapplying an active contour model, as described below. In this regard,anisotropic diffusion reduces image noise without removing significantparts of the image content, such as edges, lines or other details thatare important for the interpretation of the image. As such, anisotropicdiffusion is able to blur the image so as to smooth the sharp edges ofthe ribs that may otherwise cause difficulties during subsequentprocessing of the image. Although, anisotropic diffusion maybe appliedin various manners, the processing circuitry of an example embodiment isconfigured to subject the image to an isotropic diffusion by convolvingthe image with the two-dimensional isotropic Gaussian filter.

The PACS viewer 24 of an example embodiment also includes means, such asthe processing circuitry 32, the processor 34 the like, for applying anactive contour model to each of the left and right lungs as representedby the image, such as the image following anisotropic diffusion, so asto generate a binary image, such as an image formed solely from blackand white pixels as opposed to an image including pixels having avariety of gray levels. See block 46 of FIG. 3. In order to apply theactive contour model, the processing circuitry of an example embodimentis configured to initialize the active contour model with a maskconstructed using the representative seed points 82. As shown in FIG. 8,the mask of an example embodiment is a black image that includes twowhite regions 84 that correspond to the regions defined about therepresentative seed points. In this regard, the mask of the illustratedembodiment includes two white squares that are centered upon therepresentative seed points in the left and right lungs.

To ensure that the left and right lungs are segmented as two distinctobjects, the processing circuitry 32 of an example embodiment isconfigured to determine, for each line 60, the average of the seedpoints 80 in the left and right lungs along the respective line acrossthe image. The processing circuitry of this example embodiment thendefines a midline based upon the average value of the seed points alongeach line. For example, the midline may be defined so as to extendthrough the average value of the seed points along each line or themidline may be defined as a best fit line through the average value ofthe seed points along each line. As such, the image is divided into twoportions by the midline and the processing circuitry is then configuredto separately apply the active contour model to each portion of theimage. As known to those skilled in the art, an active contour model isa seeded region-growing algorithm that provides pixel-based imagesegmentation. The active contour model examines neighboring pixels of aninitial seed point and determines which, if any, of the neighboringpixels should be added to the region. The process is then appliediteratively. In the embodiment in which a mask as shown in FIG. 8 isapplied to an image, the processing circuitry applies the active contourmodel to each portion of the image by beginning at a respective whiteregion 84 of the mask indicative of the representative seed point 82.The active contour model then iteratively considers neighboring pixelsuntil a point is reached that the energy of the control points that makeup the active contour model is minimized with the active contour modelthen being halted. The active contour model of an example embodiment hastwo terms, one term relating to the internal energy that describes thesmoothness of the curve and another term relating to the external energythat may be determined based on the gradient of the image, the edgemagnitude, texture or intensity. As such, the active contour model mayiteratively consider neighboring pixels until a point is reached thatthe internal and external energy of the control points that make up theactive contour model is minimized. In this example embodiment, theoptimal energy state of the active contour model is obtained in aninstance in which the contour is wrapped around the desired object, thatis, the lung within the respective portion of the image. FIG. 9illustrates an example binary image of the left and right lungs that isobtained following application of the active contour model.

Following the segmentation of the left and right lungs and theapplication of the active contour model which results in a binary image,such as shown in FIG. 9, the processing circuitry 32 of an exampleembodiment is configured to remove all falsely detected objects outsideof the lung regions and to fill the black holes within the white lungregions. For example, the black regions in the otherwise white lungs ofthis example embodiment may be identified based upon their locationwithin the lung contours and/or their size relative to the surroundingwhite region, and a morphological operation, such as a hole fillingroutine, may be utilized to fill the black regions with white pixelsconsistent with the remainder of the lungs in this example.Additionally, the processing circuitry of an example embodiment isconfigured to perform morphological closing to reconnect any separatedportions of the lungs in the binary image. In an example embodiment,processing circuitry is also configured to insure separation of thelungs in the binary image by placing a thin black vertical rectangleupon the midline. Further, the processing circuitry of an exampleembodiment is configured crop the binary image around the lungs, therebycreating a binary image of the type depicted in FIG. 10.

The processing circuitry 32 of an example embodiment is also configuredto insure that the left and right lungs have been adequately segmentedin the binary image prior to further analyzing binary image. Forexample, processing circuitry of an example embodiment is configured todetermine the normalized cross correlation of the segmented binary imageand the template. If the result fails to satisfy a threshold, such as bybeing less than a predefined similarity threshold, the segmentation isnot considered adequate and further processing of the respective binaryimage may be halted. However, if the result satisfies the threshold,such as by exceeding the predefined similarity threshold, the left andright lungs of the binary image may be considered adequately segmentedand further analysis of the binary image may be performed.

The PACS viewer 24 of an example embodiment also includes means, such asthe processing circuitry 32, the processor 34 or the like, for detectinga least one feature from the binary image to permit identification ofthe left and right lungs, such as to identify which of the lungs is theleft lung and which of the lungs is the right lung. See block 48 of FIG.3. The feature detection may be performed in various manners. In anexample embodiment, the processing circuitry is configured to detect afeature from the binary image by determine the area of the left lung andthe area of the right lung from the binary image. The processingcircuitry of this example embodiment is also configured to identify theright lung as the lung within the binary image having the larger area.As such, the left lung is correspondingly identified as the lungs withinthe binary image having the smaller area.

In another example embodiment, the processing circuitry 32 is configuredto determine the location the heart. As the heart is most often on leftside of the patient's chest, the identification of the location theheart correspondingly permits the left lung to be identified as the lungin the binary image that is closest to the location of the heart.Similarly, the right lung may be identified as the lung within thebinary image that is furthest from the location of the heart. In orderto locate the heart, the processing circuitry of an example embodimentis configured to define a line between the left and right lungs asrepresented by the binary image. The line is located based upon adiaphragm of one of the lungs.

In this regard, the processing circuitry 32 is initially configured todetect the diaphragms. In order to detect the diaphragms, the processingcircuitry is again configured to divide the binary image into twoportions 92, 94 separated, for example, by the midline 88 between theleft and right lungs. The processing circuitry of this exampleembodiment is then configured to define a horizontal line segment alongthe lower edge of each portion of the binary image. Each line segment 96is then rotated as shown in FIG. 11, such as from 0° to 180°, about thecenter of rotation c where c is defined as c=(¼*binary image width,binary image height). As a result of the rotation of each line segment,the points of intersections 90 of the rotated line segment and the edgeof the lung within the respective portion of the binary image areidentified and the associated error is determined and minimized. Theseintersection points are considered points along the diaphragm, e.g.,diaphragm points. In order to identify these diaphragm points, each linesegment is rotated through an angular range, e.g., 0° to 180°, and ateach of a plurality of angular positions within the angular range, theedge points of the lung that are intersected by the line segment areinitially detected and the point closest to the center of rotation c ofthe line segment (if there is more than one edge point identified at therespective angular position) is then considered to be a diaphragm point.This process is repeated for all angular positions within the angularrange for each line segment and corresponding diaphragm points areidentified. The diaphragm points, in turn, define the diaphragmassociated with a respective lung. From among the set of diaphragmpoints identified for a respective lung, the processing circuitry isthen configured to determine the inner corner of each diaphragm, thatis, the inner corner of the diaphragm identified in each portion of thebinary image. In this regard, the inner corner of the diaphragm is thepoint identified as the diaphragm that is closest to the midline.

The processing circuitry 32 of this example embodiment is thenconfigured to define a line 100 that extends between the left and rightlungs and that is located based upon a diaphragm 98 of one of the lungs.As shown in FIG. 12, the line is defined so as to extend horizontallybetween the left and right lungs from the inner corner of the higherdiaphragm, that is, the diaphragm that is spaced further from the loweredge of the binary image. The line defined between the left and rightlungs as represented by the binary image is then divided in the firstand second lines segments 100 a, 100 b by the midline 88 that extendsbetween the left and right lungs. Each line segment extends from themidline to a respective lung. The processing circuitry of this exampleembodiment is configured to compare the length of each of the first andsecond line segments. Thereafter, the processing circuitry is configuredto identify the left lung to be on the same side of the midline of thebinary image as the longer of the first and second line segments.Correspondently, the right lung is on the same side of the midline asthe shorter of the first and second line segments.

In a further example embodiment, the processing circuitry 32 isconfigured to discriminate between the left and right lungs within thebinary image by making a first comparison of the binary image to thetemplate and a second comparison of a mirrored version of one of thebinary image or the template to the other one of the binary image or thetemplate. The mirrored version of the binary image or the template is amirrored version taken about the midline 88 between the left and rightlungs. For example, the binary image may be separately compared to thetemplate and to a mirrored version of the template. By way of example,FIG. 13 depicts a binary image 102 and the template 104 (designated +1)that may be compared, while FIG. 14 depicts the same binary image 102and a mirrored version of the template 104′, designated −1.

The processing circuitry 32 of this example embodiment is configured todetermine which of the first comparison or the second comparison isindicative of more similarity, such as by determining one of thetemplate 104 or the mirrored image of the template 104′ that is mostsimilar to the binary image 102. The processing circuitry may determinesimilarity in a various manners, but the processing circuitry of anexample embodiment determines the normalized cross correlation betweenthe binary image and the template to determine a similarity coefficientbetween and also determines the normalized cross correlation between thebinary image and the mirrored version of the template to determineanother similarity coefficient. The processing circuitry of this exampleembodiment is also configured to identify the left and right lungswithin the binary image based upon the predefined orientation of thetemplate and the one of the first or second comparison that isindicative of more similarity. In the embodiment in which the processingcircuitry determines the similarity coefficient for each of the templateand a mirrored version of the template, such as based upon thenormalized cross correlation with the binary image, the processingcircuitry is configured to identify the respective one of the templateor the mirrored version of the template that has the larger similaritycoefficient to have the same orientation as the binary image. As thetemplate has a predefined orientation such that the relative positionsof left and right lungs within the template are known to be on the leftand right sides, or vice versa, the determination of which of thetemplate of the mirrored version of the template is most similar to thebinary image permits the processing circuitry to also determine theorientation of the left and right lungs within the binary image.

Based upon the identification of the left and right lungs within thebinary image, the processing circuitry 32 is configured to predict thepatient orientation at the time that the image was captured. Theprocessing circuitry of an example embodiment is configured to notifythe user, such as a radiologist, of the predicted orientation of thepatient at the time that the image was captured. In another embodiment,however, the PACS server 24 includes means, such as the processingcircuitry, the processor 34 or the like, for comparing the patientorientation that has predicted based upon the foregoing analysis of theimage to the indication of orientation that is associated with theimage, such as the indication of orientation that is provided in theheader associated with the image study. See block 52 of FIG. 3. In thisexample embodiment, the PACS server also includes means, such as theprocessing circuitry, the processor, the user interface 38 or the like,for causing a notification to be provided in an instance in which thepatient orientation as predicted is different than the indication oforientation that is associated with the image. See block 54 of FIG. 3.As such, a user may then resolve the difference by personally reviewingthe image so as to determine the actual orientation of the image beforeproceeding with recommendations regarding the treatment of the patient.

In an example embodiment, the PACS viewer 24 includes means, such as theprocessing circuitry 32, the processor 34 or the like, for predictingpatient orientation based upon the identification of the left and rightlungs utilizing a support vector machine classifier. See block 50 ofFIG. 3. In this regard, the predicted orientation of the patient may bedetermined by plurality of feature detection techniques, such as basedupon the area of the lungs, the location of the heart and/or thesimilarity of the binary image to a template of a predefinedorientation. The processing circuitry of this example embodiment isconfigured to implement a support vector machine classifier thatconsiders the prediction of patient orientation from each of thedifferent feature detection techniques and then predicts the patientorientation based thereupon. The prediction of patient orientation by asupport vector machine classifier may be particularly useful in aninstance in which the predictions of patient orientation as determinedby a plurality of feature detection techniques are not consistent withone another.

In an embodiment in which the processing circuitry 32 implements thesupport vector machine classifier, support vector machine classifier ofan example embodiment may utilize a Gaussian radial basis function (RBF)as follows:

K(x _(i) ,x _(j))=e ^(−(γ∥x) ^(i) ^(−x) ^(j) ^(∥) ² ⁾,γ>0

wherein x_(i) is a support vector and x_(j) is a test data point. Thesupport vectors and the test data points may be defined in variousmanners. For example, the available data consisting of m+n samples maybe divided into m samples that serve as support vectors and n samplesthat serve as test samples. Additionally or alternatively, supportvectors may be defined by initially clustering the available data andthen utilizing cluster centers as support vectors. The RBF kernel hastwo parameters, γ and C>0 with unknown values beforehand. To determinethe optimal (γ, C), the processing circuitry of an example embodiment isconfigured to perform a grid search with exponentially growing sequencesof C and γ, such as Cε{2⁻⁵, 2⁻³, . . . , 2¹³, 2¹⁵}; γε{2⁻¹⁵, 2⁻¹³, . . ., 2¹, 2³}. Typically, each combination of parameter choices is checkedusing cross validation and the parameters with the best cross-validationaccuracy are selected.

The support vector machine classifier may be trained with a plurality ofimages. The plurality of the images include a first set of images thatare such that when subjected to a plurality of feature detectiontechniques cause the patient orientation to be unanimously predicted asthe same first orientation, designated +1. The plurality of the imagesalso include a second set of images that are such that when subjected tothe plurality of feature detection techniques cause the patientorientation to be unanimously predicted as the same second orientation,designated −1 (typically the opposite orientation from the firstorientation). Once trained, the support vector machine classifier asimplemented by the processing circuitry 32 of an example embodiment isconfigured to consider the prediction of patient orientation from eachof the different feature detection techniques and to then predict thepatient orientation based thereupon.

A PACS viewer 24 and an associated method and computer program productare provided in accordance with an example embodiment for identifyingpatient orientation in a reliable and automated manner. For example, thePACS viewer and associated method and computer program product of anexample embodiment are configured to discriminate between the left andright lungs of a patient based upon an image of the patient withoutreliance upon placement of a designation proximate the patient at thetime that the image was captured, burning a designation into an image ororientation of the patient at the time of image capture according to apredefined protocol. As such, the PACS viewer and associated method andcomputer program product offer reliable identification of the left andright lungs of a patient while providing greater freedom in terms of theorientation of the patient at the time that the image is captured.

As described above, FIG. 3 is a flowchart of a method, computing device30 and computer program product according to example embodiments of theinvention. It will be understood that each block of the flowchart, andcombinations of blocks in the flowchart, may be implemented by variousmeans, such as hardware and/or a computer program product comprising oneor more computer-readable mediums having computer readable programinstructions stored thereon. For example, one or more of the proceduresdescribed herein may be embodied by computer program instructions of acomputer program product. In this regard, the computer programproduct(s) which embody the procedures described herein may be stored byone or more memory devices 36 of a computing device and executed byprocessing circuitry 32 in the computing device. In some embodiments,the computer program instructions comprising the computer programproduct(s) which embody the procedures described above may be stored bymemory devices of a plurality of computing devices. As will beappreciated, any such computer program product may be loaded onto acomputer or other programmable apparatus to produce a machine, such thatthe computer program product including the instructions which execute onthe computer or other programmable apparatus creates means forimplementing the functions specified in the flowchart block(s). Further,the computer program product may comprise one or more computer-readablememories on which the computer program instructions may be stored suchthat the one or more computer-readable memories can direct a computer orother programmable apparatus to function in a particular manner, suchthat the computer program product comprises an article of manufacturewhich implements the function specified in the flowchart block(s). Thecomputer program instructions of one or more computer program productsmay also be loaded onto a computer or other programmable apparatus tocause a series of operations to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions which execute on the computer or otherprogrammable apparatus implement the functions specified in theflowchart block(s).

Accordingly, blocks or steps of the flowchart support combinations ofmeans for performing the specified functions and combinations of stepsfor performing the specified functions. It will also be understood thatone or more blocks of the flowchart, and combinations of blocks in theflowchart, may be implemented by special purpose hardware-based computersystems which perform the specified functions or steps, or combinationsof special purpose hardware and computer program product(s).

The above described functions may be carried out in many ways. Forexample, any suitable means for carrying out each of the functionsdescribed above may be employed to carry out embodiments of theinvention. In one embodiment, a suitably configured processing circuitry32 may provide all or a portion of the elements of the invention. Inanother embodiment, all or a portion of the elements of the inventionmay be configured by and operate under control of a computer programproduct. The computer program product for performing the methods ofembodiments of the invention includes a computer-readable storagemedium, such as the non-volatile storage medium, and computer-readableprogram code portions, such as a series of computer instructions,embodied in the computer-readable storage medium.

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Moreover, although the foregoing descriptions and the associateddrawings describe example embodiments in the context of certain examplecombinations of elements and/or functions, it should be appreciated thatdifferent combinations of elements and/or functions may be provided byalternative embodiments without departing from the scope of the appendedclaims. In this regard, for example, different combinations of elementsand/or functions than those explicitly described above are alsocontemplated as may be set forth in some of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

That which is claimed:
 1. A Picture Archiving and Communications System(PACS) viewer comprising processing circuitry configured to: register animage onto a template; determine a representative seed point within eachof left and right lungs as represented by the image; apply an activecontour model to each of the left and right lungs to generate a binaryimage of the left and right lungs, wherein the processing circuitry isconfigured to apply the active contour model by initializing the activecontour model with a mask constructed using the representative seedpoints; and detect at least one feature from the binary image to permitidentification of the left and right lungs.
 2. A PACS viewer accordingto claim 1 wherein the processing circuitry is configured to detect atleast one feature from the binary image by: determining an area of theleft and right lungs from the binary image; and identifying the rightlung to have the larger area.
 3. A PACS viewer according to claim 1wherein the processing circuitry is configured to detect at least onefeature from the binary image by: defining a line between the left andright lungs as represented by the binary image, wherein the line islocated based upon a diaphragm of one of the lungs; dividing the lineinto first and second line segments extending between a midline betweenthe left and right lungs and a respective lung; comparing a length ofeach of the first and second line segments; and identifying the leftlung to be on the same side of the midline as the longer of the firstand second line segments.
 4. A PACS viewer according to claim 1 whereinthe processing circuitry is configured to detect at least one featurefrom the binary image by: making a first comparison of the binary imageto the template and making a second comparison of a mirrored version ofone of the binary image or the template to the other one of the binaryimage or the template, wherein the template represents a predefinedorientation with respect to the left and right lungs; determining whichof the first comparison or the second comparison is indicative of moresimilarity; and identifying the left and right lungs within the binaryimage based upon the predefined orientation of the template and the oneof the first or second comparison that is indicative of more similarity.5. A PACS viewer according to claim 1 wherein the processing circuitryis configured to determine a representative seed point by: defining aplurality of lines extending across the image; within each of the leftand right lungs, determining a seed point on each of the plurality oflines; and determining the representative seed point within each of theleft and right lungs from among the seed points determined on each ofthe plurality of lines within each of the left and right lungs.
 6. APACS viewer according to claim 1 wherein the processing circuitry isfurther configured to subject the image to anisotropic diffusion priorto applying the active contour model.
 7. A PACS viewer according toclaim 1 wherein the processing circuitry is further configured topredict patient orientation based upon the identification of the leftand right lungs utilizing a support vector machine classifier.
 8. A PACSviewer according to claim 7 wherein the processing circuitry is furtherconfigured to: compare the patient orientation that is predicted to anindication of orientation that is associated with the image; and cause anotification to be provided in an instance in which the patientorientation that is predicted is different than the indication oforientation that is associated with the image.
 9. A method foridentifying patient orientation, the method comprising: registering animage onto a template; determining a representative seed point withineach of left and right lungs as represented by the image; applying anactive contour model to each of the left and right lungs to generate abinary image of the left and right lungs, wherein applying the activecontour model comprises initializing the active contour model with amask constructed using the representative seed points; and detecting atleast one feature from the binary image to permit identification of theleft and right lungs.
 10. A method according to claim 9 whereindetecting at least one feature from the binary image comprises:determining an area of the left and right lungs from the binary image;and identifying the right lung to have the larger area.
 11. A methodaccording to claim 9 wherein detecting at least one feature from thebinary image comprises: defining a line between the left and right lungsas represented by the binary image, wherein the line is located basedupon a diaphragm of one of the lungs; dividing the line into first andsecond line segments extending between a midline between the left andright lungs and a respective lung; comparing a length of each of thefirst and second line segments; and identifying the left lung to be onthe same side of the midline as the longer of the first and second linesegments.
 12. A method according to claim 9 wherein detecting at leastone feature from the binary image comprises: making a first comparisonof the binary image to the template and making a second comparison of amirrored version of one of the binary image or the template to the otherone of the binary image or the template, wherein the template representsa predefined orientation with respect to the left and right lungs;determining which of the first comparison or the second comparison isindicative of more similarity; and identifying the left and right lungswithin the binary image based upon the predefined orientation of thetemplate and the one of the first or second comparison that isindicative of more similarity.
 13. A method according to claim 9 whereindetermining a representative seed point comprises: defining a pluralityof lines extending across the image; within each of the left and rightlungs, determining a seed point on each of the plurality of lines; anddetermining the representative seed point within each of the left andright lungs from among the seed points determined on each of theplurality of lines within each of the left and right lungs.
 14. A methodaccording to claim 9 further comprising subjecting the image toanisotropic diffusion prior to applying the active contour model.
 15. Amethod according to claim 9 further comprising predicting patientorientation based upon the identification of the left and right lungsutilizing a support vector machine classifier.
 16. A method according toclaim 15 further comprising: comparing the patient orientation that ispredicted to an indication of orientation that is associated with theimage; and causing a notification to be provided in an instance in whichthe patient orientation that is predicted is different than theindication of orientation that is associated with the image.
 17. Acomputer program product for identifying patient orientation, thecomputer program product comprising a non-transitory computer readablestorage medium having program code portions stored thereon, the programcode portions configured, upon execution, to: register an image onto atemplate; determine a representative seed point within each of left andright lungs as represented by the image; apply an active contour modelto each of the left and right lungs to generate a binary image of theleft and right lungs, wherein applying the active contour modelcomprises initializing the active contour model with a mask constructedusing the representative seed points; and detect at least one featurefrom the binary image to permit identification of the left and rightlungs.
 18. A computer program product according to claim 17 wherein theprogram code portions configured to detect at least one feature from thebinary image comprise program code portions configured to: determine anarea of the left and right lungs from the binary image; and identify theright lung to have the larger area.
 19. A computer program productaccording to claim 17 wherein the program code portions configured todetect at least one feature from the binary image comprise program codeportions configured to: define a line between the left and right lungsas represented by the binary image, wherein the line is located basedupon a diaphragm of one of the lungs; divide the line into first andsecond line segments extending between a midline between the left andright lungs and a respective lung; compare a length of each of the firstand second line segments; and identify the left lung to be on the sameside of the midline as the longer of the first and second line segments.20. A computer program product according to claim 17 wherein the programcode portions configured to detect at least one feature from the binaryimage comprise program code portions configured to: make a firstcomparison of the binary image to the template and making a secondcomparison of a mirrored version of one of the binary image or thetemplate to the other one of the binary image or the template, whereinthe template represents a predefined orientation with respect to theleft and right lungs; determine which of the first comparison or thesecond comparison is indicative of more similarity; and identify theleft and right lungs within the binary image based upon the predefinedorientation of the template and the one of the first or secondcomparison that is indicative of more similarity.